# SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process

arXiv: https://arxiv.org/abs/2503.10466 · HTML: https://mlsanity.com/paper/2503.10466/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [A Benchmark Environment Motivated by Industrial Control Problems](https://mlsanity.com/paper/1709.09480/) (2017)
- [ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation](https://mlsanity.com/paper/2307.02991/) (2023)

## Built on by (most cited first, 3 of 3)

- [Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environments](https://mlsanity.com/paper/2507.00762/) (2025)
- [Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Control](https://mlsanity.com/paper/2510.20408/) (2025)
- [Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control](https://mlsanity.com/paper/2603.26750/) (2026)

## Abstract (arXiv)

We present a novel reinforcement learning (RL) environment designed to both optimize industrial sorting systems and study agent behavior in evolving spaces. In simulating material flow within a sorting process our environment follows the idea of a digital twin, with operational parameters like belt speed and occupancy level. To reflect real-world challenges, we integrate common upgrades to industrial setups, like new sensors or advanced machinery. It thus includes two variants: a basic version focusing on discrete belt speed adjustments and an advanced version introducing multiple sorting modes and enhanced material composition observations. We detail the observation spaces, state update mechanisms, and reward functions for both environments. We further evaluate the efficiency of common RL algorithms like Proximal Policy Optimization (PPO), Deep-Q-Networks (DQN), and Advantage Actor Critic (A2C) in comparison to a classical rule-based agent (RBA). This framework not only aids in optimizing industrial processes but also provides a foundation for studying agent behavior and transferability in evolving environments, offering insights into model performance and practical implications for real-world RL applications.